A New Breakpoint in Hybrid Particle Swarm-Neural Network Architecture: Individual Boundary Adjustment

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dc.contributor.authorCeylan, Rahime
dc.contributor.authorKoyuncu, Hasan
dc.date.accessioned2020-03-26T19:22:56Z
dc.date.available2020-03-26T19:22:56Z
dc.date.issued2016
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractNeural Network (NN) is an effective classifier, but it generally uses the Backpropagation type algorithms which are insufficient because of trapping to local minimum of error rate. For elimination of this handicap, stochastic optimization algorithms are used to update the parameters of NN. Particle Swarm Optimization (PSO) is one of these providing a robust coherence with NN. In realized studies about Hybrid PSO-NN, position and velocity boundaries of weight and bias are chosen equal or set free in space which leave the performance of PSO-NN in suspense. In this paper, the limitations of weight velocity (wv), weight position (wp), bias velocity (bv) and bias position (bp) are diversely changed and their effects on the output of hybrid structure are examined. Concerning this, the formed structure is called as Bounded PSO-NN on account of adjusting the optimum operating conditions (intervals). On performance evaluation, proposed method is tested on binary and multiclass pattern classiffication by using six medical datasets: Wisconsin Breast Cancer (WBC), Pima Indian Diabetes (PID), Bupa Liver Disorders (BLD), Heart Statlog (HS), Breast Tissue (BT) and Dermatology Data (DD). Upon analyzing the results, it was revealed that Bounded PSO-NN has a faster processing time than general PSO-NNs in which set-free and wpi=bpi and wvi=bvi conditions are settled. The superiority in terms of processing time is about 199 s (set-free) and 307 s (wpi=bpi and wvi=bvi) for training, about 16 ms (set-free) and 9ms (wpi=bpi and wvi=bvi) for test. In terms of classification performance, PSO-NN (set-free condition), PSO-NN (wpi=bpi & wvi=bvi) and PSO-NN with individual boundary adjustment (bounded PSO-NN) respectively achieves to accuracy rates as 69.84%, 95.31% and 97.22% on WBC, 47.01%, 76.69% and 77.73% on PID, 55.36%, 67.54% and 73.91% on BLD, 64.82%, 81.48% and 85.56% on HS, 75%, 92.31% and 100% on BT, 27.47%, 92.31% and 100% on DD. In the light of experiments, it is seen that Bounded PSO-NN is better than general PSO-NNs for obtaining the optimum results. Consequently, the importance of limitations is clarified and it is proven that each limitation must be adjusted individually, not be set free or not be chosen equal.en_US
dc.description.sponsorshipCoordinatorship of Selcuk University's Scientific Research ProjectsSelcuk Universityen_US
dc.description.sponsorshipThis work is supported by the Coordinatorship of Selcuk University's Scientific Research Projects.en_US
dc.identifier.doi10.1142/S0219622016500395en_US
dc.identifier.endpage1343en_US
dc.identifier.issn0219-6220en_US
dc.identifier.issn1793-6845en_US
dc.identifier.issue6en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1313en_US
dc.identifier.urihttps://dx.doi.org/10.1142/S0219622016500395
dc.identifier.urihttps://hdl.handle.net/20.500.12395/33184
dc.identifier.volume15en_US
dc.identifier.wosWOS:000389228700003en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherWORLD SCIENTIFIC PUBL CO PTE LTDen_US
dc.relation.ispartofINTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKINGen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectParticle swarm optimizationen_US
dc.subjectneural networksen_US
dc.subjecthybrid classifieren_US
dc.subjectposition-velocity intervalsen_US
dc.subjectboundary adjustmenten_US
dc.titleA New Breakpoint in Hybrid Particle Swarm-Neural Network Architecture: Individual Boundary Adjustmenten_US
dc.typeArticleen_US

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